At3g16590 Antibody

What is At3g16590 and why is it significant in plant research?

At3g16590 is one of two homologues of yeast ADA3 (Alteration/Deficiency in Activation) in Arabidopsis thaliana. It functions as a transcriptional adaptor protein involved in the SAGA (Spt-Ada-Gcn5 acetyltransferase) complex that modulates gene expression through histone acetylation. This protein plays a crucial role in flowering regulation and other developmental processes in plants. Understanding At3g16590 function provides valuable insights into plant development, stress responses, and transcriptional regulation mechanisms in plant biology. The ADA3 proteins are part of a highly conserved transcriptional regulatory pathway found across eukaryotes, making them significant for comparative studies of transcriptional control mechanisms .

At3g16590 is often studied alongside other SAGA complex components in Arabidopsis, including GCN5 histone acetyltransferase, ADA2a and ADA2b transcriptional adaptors, and SGF29 genes. Mutations in these related genes have been associated with developmental defects including dwarfism, impaired meristem development, fertility issues, and altered stress responses, suggesting the potential importance of At3g16590 in similar pathways .

What criteria should I consider when selecting an At3g16590 antibody?

When selecting an At3g16590 antibody for research, several critical factors must be evaluated to ensure experimental success:

• Target specificity: Confirm that the antibody recognizes At3g16590 specifically without cross-reactivity to related ADA3 homologues or other SAGA complex proteins in Arabidopsis. Sequence alignment and homology analysis should be performed to identify unique epitopes .

• Validation data: Review existing validation data including Western blots, immunoprecipitation results, and ChIP validation. The antibody should demonstrate specific binding to At3g16590 with minimal background .

• Application compatibility: Verify that the antibody has been validated for your specific application (Western blot, ChIP, immunofluorescence, etc.). Some antibodies perform well in certain applications but poorly in others .

• Clone type and format: Consider whether a monoclonal or polyclonal antibody is more appropriate for your research. Monoclonals offer higher specificity, while polyclonals may provide better sensitivity across multiple epitopes .

• Host species: Select an antibody raised in a species that avoids cross-reactivity with other antibodies in your experimental system, particularly important for co-localization studies .

The antibody selection process should be guided by thorough understanding of At3g16590's structure, post-translational modifications, and subcellular localization to ensure targeting of the most relevant form of the protein for your research question .

How can I validate the specificity of an At3g16590 antibody?

Validating antibody specificity is essential for obtaining reliable results. For At3g16590 antibodies, implement the following validation approaches:

Genetic Controls:

• Test the antibody on wild-type and at3g16590 mutant/knockout lines to confirm signal absence in mutants

• Include RNAi or CRISPR-edited lines with reduced At3g16590 expression to verify signal reduction

Biochemical Validation:

• Perform Western blot analysis to confirm a single band of appropriate molecular weight (check Uniprot database for predicted molecular weight)

• Conduct peptide competition assays where the antibody is pre-incubated with the immunizing peptide; this should eliminate specific signals

• Execute immunoprecipitation followed by mass spectrometry to confirm pull-down of At3g16590 protein

Cross-Reactivity Assessment:

• Test the antibody against recombinant At3g16590 and its paralog/homolog to assess potential cross-reactivity

• Include heterologous expression systems (e.g., bacteria or insect cells expressing At3g16590) as positive controls

Document all validation steps methodically in a table format:

Thorough validation should be performed for each experimental application and documented before proceeding with experimental studies .

How should I design a ChIP experiment using At3g16590 antibodies?

Chromatin immunoprecipitation (ChIP) experiments with At3g16590 antibodies require careful design to identify DNA regions bound by this transcriptional adaptor protein. Follow this methodological approach based on established protocols:

Sample Preparation:

• Harvest appropriate tissue (typically 1-2g of leaf tissue or seedlings)

• Crosslink protein-DNA interactions using 1% formaldehyde for 10 minutes

• Quench the crosslinking reaction with 0.125M glycine

• Isolate and purify nuclei using extraction buffers with protease inhibitors

• Sonicate chromatin to generate fragments of 200-500bp (optimize sonication conditions)

Immunoprecipitation:

• Pre-clear chromatin with protein A/G beads to reduce non-specific binding

• Incubate chromatin with At3g16590 antibody (typically 2-5μg) overnight at 4°C

• Capture antibody-protein-DNA complexes using protein A/G beads

• Perform stringent washing steps to remove non-specific interactions

• Elute protein-DNA complexes and reverse crosslinks at 65°C overnight

• Treat with proteinase K and RNase A, then purify DNA

Controls and Validation:

• Include a negative control (no antibody or IgG from the same species)

• Include a positive control (histone H3 antibody)

• Use At3g16590 knockout/mutant plants as biological negative controls

• Confirm enrichment at expected target genes using qPCR before proceeding to sequencing

Data Analysis:

• Normalize ChIP-qPCR data to input DNA (typically presented as percent of input)

• For histone modification studies, normalize to total H3 to account for nucleosome density

• For genome-wide studies (ChIP-seq), include spike-in controls for normalization

This experimental design allows for identification of genomic regions where At3g16590 is involved in transcriptional regulation, which can be further correlated with gene expression or histone modification data .

What experimental controls are essential when using At3g16590 antibodies in immunofluorescence microscopy?

When conducting immunofluorescence microscopy with At3g16590 antibodies, implementing appropriate controls is critical for obtaining reliable and interpretable results:

Primary Controls:

• Genetic controls: Compare wild-type plants with at3g16590 knockout/mutant lines to confirm signal specificity. The mutant should show significant reduction or absence of signal .

• Peptide competition: Pre-incubate the antibody with excess immunizing peptide to block specific binding sites. This should substantially reduce or eliminate specific signals while leaving non-specific signals intact.

• Antibody omission: Process samples without primary antibody to assess secondary antibody non-specific binding and tissue autofluorescence.

• Isotype control: Use an irrelevant antibody of the same isotype, concentration, and host species to identify non-specific binding.

Technical Controls:

• Co-localization markers: Include antibodies against known nuclear proteins (e.g., histone H3) to confirm the expected nuclear localization of At3g16590.

• Fixation and permeabilization optimization: Test multiple fixation methods (paraformaldehyde, methanol, etc.) and permeabilization conditions to ensure optimal epitope accessibility without artifacts.

• Signal amplification validation: If using signal amplification methods, include samples processed without amplification to assess signal-to-noise ratio improvements.

• Fluorophore controls: Include single-fluorophore samples when conducting multi-channel imaging to identify and correct for spectral bleed-through.

Analysis Controls:

• Quantification standardization: Use identical acquisition parameters across all samples and controls.

• Blind analysis: Have images analyzed by researchers blinded to the experimental conditions to prevent confirmation bias.

• Technical replicates: Perform staining on multiple sections from the same sample to assess technical variability.

These controls ensure that observed patterns represent genuine At3g16590 localization rather than artifacts or non-specific binding, which is particularly important given the nuclear localization of this transcriptional adaptor protein .

How do I optimize protein extraction conditions for Western blot detection of At3g16590?

Optimizing protein extraction for At3g16590 detection requires specialized approaches due to its nuclear localization and potential association with chromatin as part of the SAGA complex:

Buffer Composition Optimization:

• Nuclear extraction approach: Use a two-step extraction method:

  • First buffer: 50mM Tris-HCl pH 7.5, 150mM NaCl, 0.1% Triton X-100, 1mM EDTA

  • Second (nuclear extraction) buffer: 20mM HEPES pH 7.9, 420mM NaCl, 1.5mM MgCl₂, 0.2mM EDTA, 25% glycerol

• Detergent selection: Test multiple detergent combinations:

  • For membrane dissociation: 0.5-1% NP-40 or Triton X-100

  • For protein-protein interaction preservation: 0.1% NP-40 or 0.05% digitonin

  • For stringent extraction: 0.1% SDS (use cautiously as it may denature the protein)

• Protease and phosphatase inhibitors: Always include a comprehensive mixture:

  • Protease inhibitors: PMSF (1mM), leupeptin (10μg/mL), aprotinin (10μg/mL), pepstatin A (1μg/mL)

  • Phosphatase inhibitors: Sodium fluoride (10mM), sodium orthovanadate (1mM), β-glycerophosphate (10mM)

  • Deacetylase inhibitors: Sodium butyrate (5mM), trichostatin A (1μM)

Extraction Procedure Optimization:

• Tissue disruption: Compare mortar and pestle grinding in liquid nitrogen versus bead-based homogenization to determine optimal cell disruption method.

• Temperature conditions: Maintain samples at 4°C throughout extraction to minimize protein degradation.

• Sonication parameters: Implement brief sonication (3 cycles of 10 seconds) to enhance nuclear lysis and protein release from chromatin.

• Centrifugation protocol: Use differential centrifugation:

  • Low-speed (1,000g, 10 min) to pellet intact nuclei

  • High-speed (16,000g, 15 min) to clarify nuclear lysate

Sample Preparation for Electrophoresis:

• Denaturing conditions: Heat samples at 95°C for 5 minutes in Laemmli buffer with 100mM DTT.

• Loading amount optimization: Test protein gradients (10-50μg) to determine minimal amount needed for detection.

• Gel concentration: Use 8-10% acrylamide gels for optimal resolution of At3g16590 (molecular weight determination based on sequence analysis).

This optimized protocol significantly improves detection sensitivity compared to standard plant protein extraction methods, particularly for nuclear transcriptional regulators like At3g16590 .

How should I analyze ChIP-seq data for At3g16590 binding sites?

Analyzing ChIP-seq data for At3g16590 binding sites requires a systematic bioinformatics approach to identify genuine binding regions and connect them to biological function:

Primary Analysis Pipeline:

• Quality control of sequencing data:

  • Use FastQC to assess read quality, adapter contamination, and GC bias

  • Filter low-quality reads and trim adapters using Trimmomatic or similar tools

  • Aim for >20 million uniquely mapped reads for sufficient coverage

• Read alignment to reference genome:

  • Align to the appropriate Arabidopsis thaliana reference genome (TAIR10)

  • Use Bowtie2 or BWA with parameters optimized for short reads

  • Filter for uniquely mapped reads with MAPQ score >30

• Peak calling:

  • Use MACS2 with parameters: --gsize 135000000 --qvalue 0.05 --keep-dup auto

  • Call peaks against input DNA control and IgG control separately

  • Consider peaks found in both comparisons as high-confidence peaks

• Replicate analysis:

  • Assess reproducibility using correlation metrics (Pearson r>0.6)

  • Use IDR (Irreproducible Discovery Rate) method to identify consistent peaks

  • Create consensus peakset from reproducible peaks across replicates

Secondary Analysis:

• Peak annotation:

  • Classify peaks by genomic features (promoters, UTRs, exons, introns, intergenic)

  • Identify distance to nearest transcription start sites

  • Associate peaks with genes using tools like ChIPseeker or HOMER

• Motif discovery:

  • Use MEME-ChIP or HOMER to identify enriched DNA motifs

  • Compare discovered motifs with known transcription factor binding sites

  • Perform de novo motif discovery in peak regions

• Integration with transcriptomic data:

  • Correlate binding sites with RNA-seq data from At3g16590 mutants

  • Identify direct regulatory targets where binding correlates with expression changes

  • Classify targets by activation or repression patterns

Functional Analysis:

• Gene Ontology enrichment:

  • Perform GO term enrichment analysis of target genes

  • Identify biological processes, molecular functions, and cellular compartments

  • Use tools like agriGO specifically optimized for plant ontologies

• Pathway analysis:

  • Map targets to KEGG or Plant Reactome pathways

  • Identify enriched metabolic or signaling pathways

  • Network analysis to identify regulatory hubs

• Comparative analysis:

  • Compare At3g16590 binding sites with other SAGA complex components

  • Assess overlap with histone modification datasets (H3K14ac, H3K9ac)

  • Compare with binding profiles of related transcription factors

This comprehensive analytical approach will reveal genome-wide binding patterns of At3g16590 and its role in transcriptional regulation within Arabidopsis thaliana .

How do I troubleshoot inconsistent Western blot results with At3g16590 antibodies?

Troubleshooting inconsistent Western blot results for At3g16590 requires systematic evaluation of each experimental variable:

Sample Preparation Issues:

• Protein degradation: At3g16590 may be susceptible to proteolytic degradation.

  • Solution: Increase protease inhibitor concentration and maintain samples at 4°C

  • Add fresh inhibitors immediately before use

  • Minimize freeze-thaw cycles of protein samples

• Insufficient extraction: Nuclear proteins often require specialized extraction.

  • Solution: Use nuclear extraction buffers with higher salt concentration (300-450mM NaCl)

  • Include brief sonication steps to disrupt nuclear membranes

  • Consider sequential extraction methods to enrich nuclear proteins

• Post-translational modifications: Variable modifications may affect antibody recognition.

  • Solution: Use phosphatase inhibitors consistently

  • Consider testing antibodies that recognize different epitopes

  • Use modification-specific antibodies if studying specific PTMs

Antibody-Related Problems:

• Antibody degradation or denaturation:

  • Solution: Aliquot antibodies to minimize freeze-thaw cycles

  • Store according to manufacturer specifications

  • Check expiration dates and proper storage conditions

• Suboptimal antibody concentration:

  • Solution: Perform antibody titration (1:500, 1:1000, 1:2000, 1:5000)

  • Optimize both primary and secondary antibody concentrations

  • Consider longer incubation at 4°C (overnight) with more dilute antibody

• Blocking inefficiency:

  • Solution: Test different blocking agents (5% milk, 5% BSA, commercial blockers)

  • Extend blocking time (2-3 hours room temperature or overnight at 4°C)

  • Add 0.1% Tween-20 to reduce background

Transfer and Detection Optimization:

• Inefficient protein transfer:

  • Solution: Optimize transfer conditions (time, voltage, buffer composition)

  • Consider semi-dry vs. wet transfer for different efficiency

  • Verify transfer efficiency with reversible staining (Ponceau S)

• Detection system sensitivity:

  • Solution: Compare ECL substrates of varying sensitivity

  • Consider fluorescent secondary antibodies for more quantitative results

  • Adjust exposure times systematically

Systematic Troubleshooting Table:

Document all optimization steps systematically to establish a reliable protocol for At3g16590 detection in your specific experimental system .

What statistical approaches should I use to quantify At3g16590 expression levels across different plant tissues?

Data Collection and Normalization:

• Technical replication:

  • Perform at least three technical replicates for each biological sample

  • Use identical protein amounts for Western blot analysis

  • For RT-qPCR, perform reactions in triplicate

• Normalization strategies:

  • For Western blots: Normalize to stable reference proteins (not β-actin which varies in plants)

    • Use GAPDH, tubulin, or histone H3 as loading controls

    • Consider using total protein normalization with stain-free gels or Ponceau S

  • For RT-qPCR: Use multiple reference genes validated for stability

    • Calculate geometric mean of reference genes (GAPDH, UBQ10, EF1α)

    • Apply normalization factor to target gene expression values

• Quantification methods:

  • For Western blots: Use densitometry software (ImageJ, Image Lab)

  • For RT-qPCR: Apply 2^(-ΔΔCt) method with efficiency correction

Statistical Analysis Framework:

• Testing for normality and homogeneity of variance:

  • Apply Shapiro-Wilk test for normality

  • Use Levene's test for homogeneity of variance

  • Transform data if assumptions are violated (log transformation)

• Comparative statistics for different tissues:

  • For normally distributed data: One-way ANOVA followed by Tukey's HSD

  • For non-parametric analysis: Kruskal-Wallis followed by Dunn's test

  • Include p-value correction for multiple comparisons (Bonferroni or FDR)

• Correlation analysis:

  • Pearson correlation coefficient for linear relationships

  • Spearman's rank correlation for non-parametric data

  • Calculate confidence intervals for correlation coefficients

Advanced Statistical Approaches:

• Linear mixed-effects models:

  • Account for random effects (e.g., biological replicates)

  • Incorporate fixed effects (tissue type, developmental stage)

  • Use R packages like 'lme4' or 'nlme'

• Multivariate analysis:

  • Principal Component Analysis (PCA) to identify patterns across tissues

  • Hierarchical clustering to group tissues with similar expression patterns

  • Heatmap visualization with dendrograms

Sample Data Presentation Table:

This statistical framework provides robust quantification of At3g16590 expression patterns across tissues while accounting for biological variability and experimental error sources .

How can I use At3g16590 antibodies to investigate protein-protein interactions within the SAGA complex?

Investigating protein-protein interactions (PPIs) between At3g16590 and other components of the SAGA complex requires sophisticated biochemical approaches that preserve native interactions:

Co-Immunoprecipitation (Co-IP) Strategy:

• Gentle extraction protocol:

  • Use low-detergent buffers (0.1% NP-40 or 0.1% Triton X-100)

  • Include stabilizing agents (5-10% glycerol)

  • Maintain physiological salt concentrations (100-150mM NaCl)

  • Add protease and phosphatase inhibitors to preserve modifications

• IP optimization:

  • Pre-clear lysates with Protein A/G beads to reduce background

  • Use 2-5μg At3g16590 antibody per 500μg protein lysate

  • Incubate overnight at 4°C with gentle rotation

  • Include negative controls: IgG control, lysate from knockout plants

• Washing conditions:

  • Implement progressively stringent washes (increasing salt or detergent)

  • Monitor complex stability across washing conditions

  • Preserve sample fractions from each wash for troubleshooting

• Detection strategy:

  • Immunoblot for known SAGA components (GCN5, ADA2a/b, SGF29)

  • Use clean, validated antibodies for each target protein

  • Consider reciprocal Co-IPs to confirm interactions

Advanced Interaction Analysis Techniques:

• Proximity Ligation Assay (PLA):

  • Visualize protein-protein interactions in situ

  • Fixed plant cells are incubated with primary antibodies against At3g16590 and interaction partners

  • Secondary antibodies with attached DNA probes enable amplification if proteins are in proximity (<40nm)

  • Fluorescent signal indicates interaction in cellular context

• Bimolecular Fluorescence Complementation (BiFC):

  • Clone At3g16590 and potential interactors into BiFC vectors

  • Express in protoplasts or stable transgenic plants

  • Reconstituted YFP/GFP fluorescence confirms interaction

  • Allows visualization of interaction subcellular localization

• Tandem Affinity Purification (TAP):

  • Generate transgenic plants expressing TAP-tagged At3g16590

  • Perform sequential purification steps to isolate intact complexes

  • Identify components by mass spectrometry

  • Quantify relative abundance of interactors

Mass Spectrometry Workflow for Complex Analysis:

• Sample preparation:

  • Perform immunoprecipitation with At3g16590 antibodies

  • Digest samples with trypsin or LysC proteases

  • Fractionate peptides using liquid chromatography

• MS analysis parameters:

  • Use nanoLC-MS/MS for high sensitivity

  • Implement data-dependent acquisition

  • Consider SWATH-MS for quantitative comparison

• Data analysis approach:

  • Search against Arabidopsis thaliana protein database

  • Filter for high-confidence identifications (FDR <1%)

  • Calculate enrichment ratios compared to control IPs

  • Visualize interaction networks using Cytoscape or similar tools

This comprehensive approach will reveal the composition and dynamics of At3g16590-containing SAGA complexes in Arabidopsis thaliana and provide insights into their functional roles in transcriptional regulation .

What approaches can I use to study At3g16590 post-translational modifications?

Post-translational modifications (PTMs) of At3g16590 can significantly impact its function as a transcriptional adaptor. Here's a comprehensive approach to studying these modifications:

Identification of Potential Modifications:

• In silico prediction:

  • Use prediction tools (NetPhos, UbPred, SUMOsp) to identify potential modification sites

  • Analyze protein sequence for conserved modification motifs

  • Compare with known modifications in yeast and mammalian ADA3 homologs

• Global proteomic screening:

  • Perform immunoprecipitation of At3g16590 followed by LC-MS/MS

  • Use enrichment techniques for specific modifications:

    • Phosphorylation: TiO₂ or IMAC enrichment

    • Ubiquitination: K-ε-GG antibody enrichment

    • Acetylation: Acetyl-lysine antibody enrichment

  • Include proteasome inhibitors (MG132) to detect ubiquitination

Validation and Characterization Methods:

• Site-specific antibody approach:

  • Generate antibodies against predicted modification sites

  • Validate antibody specificity using synthetic modified peptides

  • Apply in Western blot and immunofluorescence experiments

• Genetic approaches:

  • Generate site-specific mutants (e.g., S→A for phosphorylation, K→R for acetylation/ubiquitination)

  • Express mutant forms in at3g16590 knockout background

  • Assess functional consequences through phenotypic analysis

• Pharmacological approaches:

  • Treat plants with inhibitors of specific PTM enzymes:

    • Phosphorylation: Kinase inhibitors (staurosporine)

    • Acetylation: HDAC inhibitors (TSA, sodium butyrate)

    • Ubiquitination: Proteasome inhibitors (MG132, bortezomib)

  • Monitor changes in At3g16590 modification state

Analysis of Modification Dynamics:

• Developmental profiling:

  • Compare modification patterns across developmental stages

  • Correlate modifications with developmental transitions

  • Analyze tissue-specific modification patterns

• Stress response analysis:

  • Expose plants to various stresses (drought, heat, pathogens)

  • Monitor changes in At3g16590 modification state

  • Correlate modifications with transcriptional responses

• Circadian/diurnal regulation:

  • Sample plants across time-course (every 4 hours for 24-48 hours)

  • Quantify modification levels at each timepoint

  • Correlate with gene expression patterns

Functional Impact Assessment:

• Chromatin association:

  • Perform ChIP using antibodies against modified forms

  • Compare binding profiles of modified vs. unmodified protein

  • Correlate modifications with target gene expression

• Protein interaction changes:

  • Compare interaction partners of modified vs. unmodified At3g16590

  • Assess impact on SAGA complex assembly and stability

  • Identify modification-dependent interactions

• Enzymatic activity effects:

  • Measure histone acetyltransferase activity of SAGA complex

  • Compare activity with wild-type vs. modification-site mutants

  • Assess impact on target histone modification levels (H3K14ac)

These approaches provide a comprehensive framework for studying At3g16590 post-translational modifications and their functional significance in plant development and stress responses.

How can I use At3g16590 antibodies to study its role in histone modification and chromatin remodeling?

At3g16590, as a component of the SAGA complex, is intricately involved in histone modification and chromatin remodeling. Here's a methodological framework for investigating these functions:

Chromatin Immunoprecipitation-Based Approaches:

• Sequential ChIP (ChIP-reChIP):

  • First round: Immunoprecipitate with At3g16590 antibody

  • Elute complexes under mild conditions

  • Second round: Immunoprecipitate with antibodies against histone modifications (H3K14ac, H3K9ac)

  • Analysis reveals regions where At3g16590 and specific histone marks co-occur

• ChIP followed by histone modification analysis:

  • Perform ChIP with At3g16590 antibody

  • Analyze precipitated material for histone modifications

  • Compare modification patterns to input chromatin

  • Quantify enrichment of specific modifications at At3g16590-bound regions

• Genome-wide epigenetic profiling:

  • Comparative ChIP-seq for histone modifications in wild-type vs. at3g16590 mutants

  • Focus on activating marks (H3K4me3, H3K9ac, H3K14ac, H3K36me3)

  • Create integrated epigenomic maps to visualize modification changes

  • Correlate modification changes with transcriptional alterations

In vitro Biochemical Assays:

• Histone acetyltransferase (HAT) activity assays:

  • Immunoprecipitate SAGA complex using At3g16590 antibody

  • Incubate with recombinant histones or nucleosomes

  • Measure acetylation by Western blot or radioactive assay

  • Compare activity between wild-type and mutant plants

• Chromatin remodeling assays:

  • Prepare mononucleosomes with positioned nucleosomes

  • Incubate with immunopurified At3g16590-containing complexes

  • Analyze nucleosome positioning changes by MNase digestion

  • Assess accessibility changes using restriction enzyme accessibility

Cell-Based Functional Approaches:

• Chromatin accessibility analysis:

  • Perform ATAC-seq in wild-type and at3g16590 mutant plants

  • Compare chromatin accessibility profiles

  • Identify regions requiring At3g16590 for proper chromatin structure

  • Correlate accessibility changes with binding sites and gene expression

• Live-cell imaging of chromatin dynamics:

  • Generate fluorescently tagged At3g16590 constructs

  • Create reporter lines for visualization of chromatin states

  • Perform FRAP (Fluorescence Recovery After Photobleaching) to measure dynamics

  • Analyze co-localization with chromatin marks in real-time

Integrative Data Analysis:

• Multi-omics integration approach:

  • Combine datasets: ChIP-seq, RNA-seq, ATAC-seq, histone PTM mapping

  • Identify direct vs. indirect effects of At3g16590 activity

  • Create visual models of At3g16590-dependent chromatin regulation

  • Generate testable hypotheses about mechanism of action

• Comparison with other SAGA components:

  • Perform parallel analyses with GCN5, ADA2a/b, and SGF29

  • Identify shared vs. unique functions of At3g16590

  • Assess interdependence of complex components for function

  • Create a hierarchical model of SAGA complex assembly and function

This methodological framework enables comprehensive investigation of At3g16590's role in chromatin regulation, providing insights into how this adaptor protein contributes to transcriptional control in plants through epigenetic mechanisms .